Overview

Dataset statistics

Number of variables38
Number of observations17333
Missing cells3126
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.9 MiB
Average record size in memory297.0 B

Variable types

Numeric9
Categorical9
Text15
Boolean3
DateTime2

Alerts

CHANGE_PASSWORD_x has constant value ""Constant
ROLE_y has constant value ""Constant
CHANGE_PASSWORD_y has constant value ""Constant
CHANGE_PASSWORD has constant value ""Constant
ROLE_x is highly imbalanced (99.9%)Imbalance
ROLE is highly imbalanced (99.9%)Imbalance

Reproduction

Analysis started2024-04-15 16:48:03.760452
Analysis finished2024-04-15 16:48:18.037424
Duration14.28 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

USER_SKILL_ID
Real number (ℝ)

Distinct1042
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean468.76888
Minimum1
Maximum1100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size135.5 KiB
2024-04-15T22:18:18.132034image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile34
Q1233
median466
Q3687
95-th percentile970
Maximum1100
Range1099
Interquartile range (IQR)454

Descriptive statistics

Standard deviation283.21623
Coefficient of variation (CV)0.60417029
Kurtosis-0.91465932
Mean468.76888
Median Absolute Deviation (MAD)230
Skewness0.1794105
Sum8125171
Variance80211.434
MonotonicityIncreasing
2024-04-15T22:18:18.305483image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
662 156
 
0.9%
893 156
 
0.9%
18 156
 
0.9%
16 156
 
0.9%
472 156
 
0.9%
477 156
 
0.9%
274 156
 
0.9%
158 156
 
0.9%
351 156
 
0.9%
727 81
 
0.5%
Other values (1032) 15848
91.4%
ValueCountFrequency (%)
1 5
 
< 0.1%
2 55
0.3%
3 35
0.2%
4 12
 
0.1%
5 55
0.3%
6 24
0.1%
7 16
 
0.1%
9 6
 
< 0.1%
10 16
 
0.1%
11 3
 
< 0.1%
ValueCountFrequency (%)
1100 4
 
< 0.1%
1099 1
 
< 0.1%
1098 1
 
< 0.1%
1097 4
 
< 0.1%
1096 1
 
< 0.1%
1095 1
 
< 0.1%
1094 20
0.1%
1093 1
 
< 0.1%
1092 1
 
< 0.1%
1091 1
 
< 0.1%

SKILL_ID
Real number (ℝ)

Distinct50
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.589107
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size135.5 KiB
2024-04-15T22:18:18.454757image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q115
median27
Q339
95-th percentile48
Maximum50
Range49
Interquartile range (IQR)24

Descriptive statistics

Standard deviation14.394121
Coefficient of variation (CV)0.54135405
Kurtosis-1.1097587
Mean26.589107
Median Absolute Deviation (MAD)12
Skewness-0.11502807
Sum460869
Variance207.19072
MonotonicityNot monotonic
2024-04-15T22:18:18.630838image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 536
 
3.1%
27 505
 
2.9%
2 502
 
2.9%
16 498
 
2.9%
36 486
 
2.8%
21 479
 
2.8%
32 462
 
2.7%
48 455
 
2.6%
47 453
 
2.6%
35 445
 
2.6%
Other values (40) 12512
72.2%
ValueCountFrequency (%)
1 344
2.0%
2 502
2.9%
3 336
1.9%
4 329
1.9%
5 274
1.6%
6 276
1.6%
7 237
1.4%
8 216
1.2%
9 335
1.9%
10 295
1.7%
ValueCountFrequency (%)
50 396
2.3%
49 371
2.1%
48 455
2.6%
47 453
2.6%
46 413
2.4%
45 305
1.8%
44 307
1.8%
43 395
2.3%
42 361
2.1%
41 354
2.0%

USER_ID
Real number (ℝ)

Distinct386
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean236.49132
Minimum11
Maximum510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size135.5 KiB
2024-04-15T22:18:18.774125image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile35
Q1101
median203
Q3384
95-th percentile484
Maximum510
Range499
Interquartile range (IQR)283

Descriptive statistics

Standard deviation146.37475
Coefficient of variation (CV)0.61894343
Kurtosis-1.2915134
Mean236.49132
Median Absolute Deviation (MAD)123
Skewness0.25283238
Sum4099104
Variance21425.567
MonotonicityNot monotonic
2024-04-15T22:18:18.927150image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 1404
 
8.1%
398 693
 
4.0%
53 486
 
2.8%
389 448
 
2.6%
158 385
 
2.2%
35 378
 
2.2%
148 360
 
2.1%
423 352
 
2.0%
179 294
 
1.7%
408 252
 
1.5%
Other values (376) 12281
70.9%
ValueCountFrequency (%)
11 1
 
< 0.1%
12 45
 
0.3%
13 48
 
0.3%
14 1
 
< 0.1%
15 1
 
< 0.1%
16 168
1.0%
17 1
 
< 0.1%
18 20
 
0.1%
19 1
 
< 0.1%
20 1
 
< 0.1%
ValueCountFrequency (%)
510 1
 
< 0.1%
509 60
0.3%
508 45
0.3%
507 1
 
< 0.1%
506 30
0.2%
505 8
 
< 0.1%
504 8
 
< 0.1%
503 1
 
< 0.1%
502 16
 
0.1%
501 1
 
< 0.1%

SKILL_LEVEL
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size135.5 KiB
Intermediate
6317 
Advance
5845 
Beginner
5171 

Length

Max length12
Median length8
Mean length9.1205792
Min length7

Characters and Unicode

Total characters158087
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAdvance
2nd rowAdvance
3rd rowAdvance
4th rowAdvance
5th rowAdvance

Common Values

ValueCountFrequency (%)
Intermediate 6317
36.4%
Advance 5845
33.7%
Beginner 5171
29.8%

Length

2024-04-15T22:18:19.233894image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-15T22:18:19.356105image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
intermediate 6317
36.4%
advance 5845
33.7%
beginner 5171
29.8%

Most occurring characters

ValueCountFrequency (%)
e 35138
22.2%
n 22504
14.2%
t 12634
 
8.0%
d 12162
 
7.7%
a 12162
 
7.7%
r 11488
 
7.3%
i 11488
 
7.3%
I 6317
 
4.0%
m 6317
 
4.0%
A 5845
 
3.7%
Other values (4) 22032
13.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 158087
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 35138
22.2%
n 22504
14.2%
t 12634
 
8.0%
d 12162
 
7.7%
a 12162
 
7.7%
r 11488
 
7.3%
i 11488
 
7.3%
I 6317
 
4.0%
m 6317
 
4.0%
A 5845
 
3.7%
Other values (4) 22032
13.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 158087
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 35138
22.2%
n 22504
14.2%
t 12634
 
8.0%
d 12162
 
7.7%
a 12162
 
7.7%
r 11488
 
7.3%
i 11488
 
7.3%
I 6317
 
4.0%
m 6317
 
4.0%
A 5845
 
3.7%
Other values (4) 22032
13.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 158087
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 35138
22.2%
n 22504
14.2%
t 12634
 
8.0%
d 12162
 
7.7%
a 12162
 
7.7%
r 11488
 
7.3%
i 11488
 
7.3%
I 6317
 
4.0%
m 6317
 
4.0%
A 5845
 
3.7%
Other values (4) 22032
13.9%

STATUS_x
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size135.5 KiB
Rejected
6197 
Approved
5892 
Pending
5244 

Length

Max length8
Median length8
Mean length7.6974557
Min length7

Characters and Unicode

Total characters133420
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowApproved
2nd rowApproved
3rd rowApproved
4th rowApproved
5th rowApproved

Common Values

ValueCountFrequency (%)
Rejected 6197
35.8%
Approved 5892
34.0%
Pending 5244
30.3%

Length

2024-04-15T22:18:19.488293image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-15T22:18:19.602014image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
rejected 6197
35.8%
approved 5892
34.0%
pending 5244
30.3%

Most occurring characters

ValueCountFrequency (%)
e 29727
22.3%
d 17333
13.0%
p 11784
 
8.8%
n 10488
 
7.9%
R 6197
 
4.6%
j 6197
 
4.6%
c 6197
 
4.6%
t 6197
 
4.6%
A 5892
 
4.4%
r 5892
 
4.4%
Other values (5) 27516
20.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 133420
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 29727
22.3%
d 17333
13.0%
p 11784
 
8.8%
n 10488
 
7.9%
R 6197
 
4.6%
j 6197
 
4.6%
c 6197
 
4.6%
t 6197
 
4.6%
A 5892
 
4.4%
r 5892
 
4.4%
Other values (5) 27516
20.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 133420
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 29727
22.3%
d 17333
13.0%
p 11784
 
8.8%
n 10488
 
7.9%
R 6197
 
4.6%
j 6197
 
4.6%
c 6197
 
4.6%
t 6197
 
4.6%
A 5892
 
4.4%
r 5892
 
4.4%
Other values (5) 27516
20.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 133420
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 29727
22.3%
d 17333
13.0%
p 11784
 
8.8%
n 10488
 
7.9%
R 6197
 
4.6%
j 6197
 
4.6%
c 6197
 
4.6%
t 6197
 
4.6%
A 5892
 
4.4%
r 5892
 
4.4%
Other values (5) 27516
20.6%

SKILL_NAME
Categorical

Distinct49
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size135.5 KiB
GraphQL
 
732
Git
 
536
Docker
 
505
Java
 
502
Rust
 
498
Other values (44)
14560 

Length

Max length52
Median length19
Mean length11.015635
Min length2

Characters and Unicode

Total characters190934
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKanban
2nd rowKanban
3rd rowKanban
4th rowKanban
5th rowKanban

Common Values

ValueCountFrequency (%)
GraphQL 732
 
4.2%
Git 536
 
3.1%
Docker 505
 
2.9%
Java 502
 
2.9%
Rust 498
 
2.9%
Behavior-Driven Development (BDD) 486
 
2.8%
Flask 479
 
2.8%
Scrum 462
 
2.7%
Robotics 455
 
2.6%
Computer Vision 453
 
2.6%
Other values (39) 12225
70.5%

Length

2024-04-15T22:18:19.770923image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
development 931
 
3.6%
graphql 732
 
2.9%
data 612
 
2.4%
c 578
 
2.3%
git 536
 
2.1%
docker 505
 
2.0%
java 502
 
2.0%
rust 498
 
1.9%
behavior-driven 486
 
1.9%
bdd 486
 
1.9%
Other values (60) 19799
77.1%

Most occurring characters

ValueCountFrequency (%)
e 18027
 
9.4%
n 12084
 
6.3%
i 11171
 
5.9%
t 11057
 
5.8%
a 10663
 
5.6%
r 9761
 
5.1%
o 9447
 
4.9%
s 8879
 
4.7%
8332
 
4.4%
c 6208
 
3.3%
Other values (44) 85305
44.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 190934
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 18027
 
9.4%
n 12084
 
6.3%
i 11171
 
5.9%
t 11057
 
5.8%
a 10663
 
5.6%
r 9761
 
5.1%
o 9447
 
4.9%
s 8879
 
4.7%
8332
 
4.4%
c 6208
 
3.3%
Other values (44) 85305
44.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 190934
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 18027
 
9.4%
n 12084
 
6.3%
i 11171
 
5.9%
t 11057
 
5.8%
a 10663
 
5.6%
r 9761
 
5.1%
o 9447
 
4.9%
s 8879
 
4.7%
8332
 
4.4%
c 6208
 
3.3%
Other values (44) 85305
44.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 190934
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 18027
 
9.4%
n 12084
 
6.3%
i 11171
 
5.9%
t 11057
 
5.8%
a 10663
 
5.6%
r 9761
 
5.1%
o 9447
 
4.9%
s 8879
 
4.7%
8332
 
4.4%
c 6208
 
3.3%
Other values (44) 85305
44.7%
Distinct386
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size135.5 KiB
2024-04-15T22:18:19.999131image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length33
Median length30
Mean length22.057059
Min length13

Characters and Unicode

Total characters382315
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique160 ?
Unique (%)0.9%

Sample

1st rowmmccauley4e@nytimes.com
2nd rowmmccauley4e@nytimes.com
3rd rowmmccauley4e@nytimes.com
4th rowmmccauley4e@nytimes.com
5th rowmmccauley4e@nytimes.com
ValueCountFrequency (%)
bbarbera1x@usatoday.com 1404
 
8.1%
lloadar@miibeian.gov.cn 693
 
4.0%
srigden16@indiegogo.com 486
 
2.8%
gwaslinai@tumblr.com 448
 
2.6%
khaggus43@imageshack.us 385
 
2.2%
nmccurlyeo@uol.com.br 378
 
2.2%
cshelton3t@odnoklassniki.ru 360
 
2.1%
vdahlgrenbg@friendfeed.com 352
 
2.0%
syarrington4o@zdnet.com 294
 
1.7%
agerriessenb1@examiner.com 252
 
1.5%
Other values (376) 12281
70.9%
2024-04-15T22:18:20.403412image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 31683
 
8.3%
e 27942
 
7.3%
a 27213
 
7.1%
c 23525
 
6.2%
r 21636
 
5.7%
m 19952
 
5.2%
n 19437
 
5.1%
. 19381
 
5.1%
i 18884
 
4.9%
l 17422
 
4.6%
Other values (29) 155240
40.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 382315
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 31683
 
8.3%
e 27942
 
7.3%
a 27213
 
7.1%
c 23525
 
6.2%
r 21636
 
5.7%
m 19952
 
5.2%
n 19437
 
5.1%
. 19381
 
5.1%
i 18884
 
4.9%
l 17422
 
4.6%
Other values (29) 155240
40.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 382315
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 31683
 
8.3%
e 27942
 
7.3%
a 27213
 
7.1%
c 23525
 
6.2%
r 21636
 
5.7%
m 19952
 
5.2%
n 19437
 
5.1%
. 19381
 
5.1%
i 18884
 
4.9%
l 17422
 
4.6%
Other values (29) 155240
40.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 382315
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 31683
 
8.3%
e 27942
 
7.3%
a 27213
 
7.1%
c 23525
 
6.2%
r 21636
 
5.7%
m 19952
 
5.2%
n 19437
 
5.1%
. 19381
 
5.1%
i 18884
 
4.9%
l 17422
 
4.6%
Other values (29) 155240
40.6%

ROLE_x
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size135.5 KiB
user
17332 
admin
 
1

Length

Max length5
Median length4
Mean length4.0000577
Min length4

Characters and Unicode

Total characters69333
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowuser
2nd rowuser
3rd rowuser
4th rowuser
5th rowuser

Common Values

ValueCountFrequency (%)
user 17332
> 99.9%
admin 1
 
< 0.1%

Length

2024-04-15T22:18:20.563531image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-15T22:18:20.669923image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
user 17332
> 99.9%
admin 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
u 17332
25.0%
s 17332
25.0%
e 17332
25.0%
r 17332
25.0%
a 1
 
< 0.1%
d 1
 
< 0.1%
m 1
 
< 0.1%
i 1
 
< 0.1%
n 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 69333
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u 17332
25.0%
s 17332
25.0%
e 17332
25.0%
r 17332
25.0%
a 1
 
< 0.1%
d 1
 
< 0.1%
m 1
 
< 0.1%
i 1
 
< 0.1%
n 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 69333
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u 17332
25.0%
s 17332
25.0%
e 17332
25.0%
r 17332
25.0%
a 1
 
< 0.1%
d 1
 
< 0.1%
m 1
 
< 0.1%
i 1
 
< 0.1%
n 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 69333
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u 17332
25.0%
s 17332
25.0%
e 17332
25.0%
r 17332
25.0%
a 1
 
< 0.1%
d 1
 
< 0.1%
m 1
 
< 0.1%
i 1
 
< 0.1%
n 1
 
< 0.1%

PHONE_NUMBER_x
Real number (ℝ)

Distinct386
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0488976 × 109
Minimum1.0049387 × 109
Maximum9.9960208 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size135.5 KiB
2024-04-15T22:18:20.793831image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.0049387 × 109
5-th percentile1.3024693 × 109
Q13.6052395 × 109
median6.2831165 × 109
Q38.6441764 × 109
95-th percentile9.52621 × 109
Maximum9.9960208 × 109
Range8.9910821 × 109
Interquartile range (IQR)5.038937 × 109

Descriptive statistics

Standard deviation2.6811929 × 109
Coefficient of variation (CV)0.44325315
Kurtosis-1.1202387
Mean6.0488976 × 109
Median Absolute Deviation (MAD)2.3936422 × 109
Skewness-0.2903308
Sum1.0484554 × 1014
Variance7.1887955 × 1018
MonotonicityNot monotonic
2024-04-15T22:18:20.941461image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9224743888 1404
 
8.1%
3605239489 693
 
4.0%
5811418659 486
 
2.8%
5913549527 448
 
2.6%
9526209988 385
 
2.2%
7129119794 378
 
2.2%
4328864394 360
 
2.1%
7743556574 352
 
2.0%
3587136794 294
 
1.7%
6321637239 252
 
1.5%
Other values (376) 12281
70.9%
ValueCountFrequency (%)
1004938722 96
0.6%
1008868059 1
 
< 0.1%
1066117899 189
1.1%
1092149162 1
 
< 0.1%
1133247041 72
 
0.4%
1172341225 1
 
< 0.1%
1178022515 168
1.0%
1195035976 1
 
< 0.1%
1201122077 1
 
< 0.1%
1203986793 60
 
0.3%
ValueCountFrequency (%)
9996020840 1
 
< 0.1%
9966994472 36
 
0.2%
9961356509 48
 
0.3%
9906928741 64
 
0.4%
9901322391 36
 
0.2%
9877161571 36
 
0.2%
9872962156 160
0.9%
9872017012 180
1.0%
9854767904 1
 
< 0.1%
9822908387 1
 
< 0.1%
Distinct386
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size135.5 KiB
2024-04-15T22:18:21.173664image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length16
Median length13
Mean length11.744418
Min length7

Characters and Unicode

Total characters203566
Distinct characters88
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique160 ?
Unique (%)0.9%

Sample

1st rowyJ2%A#X",ky0Cp
2nd rowyJ2%A#X",ky0Cp
3rd rowyJ2%A#X",ky0Cp
4th rowyJ2%A#X",ky0Cp
5th rowyJ2%A#X",ky0Cp
ValueCountFrequency (%)
yj4)wln9l 1404
 
8.1%
qb7!|%wl@\gp 693
 
4.0%
en4<&*wf 486
 
2.8%
qm9|mq|t`y 448
 
2.6%
uo2",%k5r$b@4l 385
 
2.2%
jb9=ssb/_m 378
 
2.2%
ja1*_,ath3 360
 
2.1%
gh5>xf+>vk 352
 
2.0%
vx6>pkus?!'~n7uc 294
 
1.7%
tf0(h1e_|yjdyw 252
 
1.5%
Other values (376) 12281
70.9%
2024-04-15T22:18:21.552509image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
L 6265
 
3.1%
9 6018
 
3.0%
N 4339
 
2.1%
4 4320
 
2.1%
| 4092
 
2.0%
y 3959
 
1.9%
w 3910
 
1.9%
X 3505
 
1.7%
` 3383
 
1.7%
1 3379
 
1.7%
Other values (78) 160396
78.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 203566
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 6265
 
3.1%
9 6018
 
3.0%
N 4339
 
2.1%
4 4320
 
2.1%
| 4092
 
2.0%
y 3959
 
1.9%
w 3910
 
1.9%
X 3505
 
1.7%
` 3383
 
1.7%
1 3379
 
1.7%
Other values (78) 160396
78.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 203566
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 6265
 
3.1%
9 6018
 
3.0%
N 4339
 
2.1%
4 4320
 
2.1%
| 4092
 
2.0%
y 3959
 
1.9%
w 3910
 
1.9%
X 3505
 
1.7%
` 3383
 
1.7%
1 3379
 
1.7%
Other values (78) 160396
78.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 203566
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 6265
 
3.1%
9 6018
 
3.0%
N 4339
 
2.1%
4 4320
 
2.1%
| 4092
 
2.0%
y 3959
 
1.9%
w 3910
 
1.9%
X 3505
 
1.7%
` 3383
 
1.7%
1 3379
 
1.7%
Other values (78) 160396
78.8%

CHANGE_PASSWORD_x
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.1 KiB
True
17333 
ValueCountFrequency (%)
True 17333
100.0%
2024-04-15T22:18:21.684313image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Distinct386
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size135.5 KiB
2024-04-15T22:18:21.963413image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length21
Median length19
Mean length14.052732
Min length8

Characters and Unicode

Total characters243576
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique160 ?
Unique (%)0.9%

Sample

1st rowMaximo McCauley
2nd rowMaximo McCauley
3rd rowMaximo McCauley
4th rowMaximo McCauley
5th rowMaximo McCauley
ValueCountFrequency (%)
brantley 1404
 
4.0%
barbera 1404
 
4.0%
lettie 693
 
2.0%
load 693
 
2.0%
shoshanna 486
 
1.4%
rigden 486
 
1.4%
vin 472
 
1.4%
grantham 448
 
1.3%
waslin 448
 
1.3%
kania 385
 
1.1%
Other values (751) 27785
80.1%
2024-04-15T22:18:22.407719image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 24824
 
10.2%
e 24331
 
10.0%
r 18551
 
7.6%
n 18316
 
7.5%
17371
 
7.1%
i 14893
 
6.1%
l 14755
 
6.1%
o 11014
 
4.5%
t 10746
 
4.4%
y 6951
 
2.9%
Other values (43) 81824
33.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 243576
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 24824
 
10.2%
e 24331
 
10.0%
r 18551
 
7.6%
n 18316
 
7.5%
17371
 
7.1%
i 14893
 
6.1%
l 14755
 
6.1%
o 11014
 
4.5%
t 10746
 
4.4%
y 6951
 
2.9%
Other values (43) 81824
33.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 243576
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 24824
 
10.2%
e 24331
 
10.0%
r 18551
 
7.6%
n 18316
 
7.5%
17371
 
7.1%
i 14893
 
6.1%
l 14755
 
6.1%
o 11014
 
4.5%
t 10746
 
4.4%
y 6951
 
2.9%
Other values (43) 81824
33.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 243576
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 24824
 
10.2%
e 24331
 
10.0%
r 18551
 
7.6%
n 18316
 
7.5%
17371
 
7.1%
i 14893
 
6.1%
l 14755
 
6.1%
o 11014
 
4.5%
t 10746
 
4.4%
y 6951
 
2.9%
Other values (43) 81824
33.6%

USER_PROJECT_ID
Real number (ℝ)

Distinct1039
Distinct (%)6.0%
Missing68
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean524.87883
Minimum1
Maximum1200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size135.5 KiB
2024-04-15T22:18:22.572296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile38
Q1260
median520
Q3778
95-th percentile1084
Maximum1200
Range1199
Interquartile range (IQR)518

Descriptive statistics

Standard deviation316.34722
Coefficient of variation (CV)0.60270523
Kurtosis-0.95367264
Mean524.87883
Median Absolute Deviation (MAD)259
Skewness0.14735274
Sum9062033
Variance100075.56
MonotonicityNot monotonic
2024-04-15T22:18:22.725726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
107 117
 
0.7%
1084 117
 
0.7%
328 117
 
0.7%
593 117
 
0.7%
726 117
 
0.7%
804 117
 
0.7%
835 117
 
0.7%
850 117
 
0.7%
294 117
 
0.7%
20 117
 
0.7%
Other values (1029) 16095
92.9%
ValueCountFrequency (%)
1 9
 
0.1%
2 5
 
< 0.1%
3 24
 
0.1%
4 16
 
0.1%
6 45
0.3%
7 24
 
0.1%
8 4
 
< 0.1%
9 6
 
< 0.1%
10 4
 
< 0.1%
11 63
0.4%
ValueCountFrequency (%)
1200 1
 
< 0.1%
1199 1
 
< 0.1%
1198 1
 
< 0.1%
1197 6
 
< 0.1%
1195 8
< 0.1%
1192 9
0.1%
1190 1
 
< 0.1%
1189 16
0.1%
1188 1
 
< 0.1%
1187 5
 
< 0.1%
Distinct71
Distinct (%)0.4%
Missing68
Missing (%)0.4%
Memory size135.5 KiB
2024-04-15T22:18:22.932826image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length16
Median length15
Mean length9.8162757
Min length7

Characters and Unicode

Total characters169478
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowByteHub
2nd rowCyberSphere
3rd rowDataBlast
4th rowByteGenius
5th rowCodeBlaze
ValueCountFrequency (%)
pixelcloud 918
 
5.3%
cyberblast 634
 
3.7%
bytehub 590
 
3.4%
codegenius 536
 
3.1%
innovatematrix 527
 
3.1%
codesavvy 524
 
3.0%
pixelnest 514
 
3.0%
technest 504
 
2.9%
codepulse 475
 
2.8%
appsphere 464
 
2.7%
Other values (61) 11579
67.1%
2024-04-15T22:18:23.287602image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 24263
 
14.3%
a 13063
 
7.7%
t 11481
 
6.8%
o 9085
 
5.4%
C 8673
 
5.1%
r 8390
 
5.0%
s 7268
 
4.3%
l 6872
 
4.1%
i 6318
 
3.7%
n 5697
 
3.4%
Other values (30) 68368
40.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 169478
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 24263
 
14.3%
a 13063
 
7.7%
t 11481
 
6.8%
o 9085
 
5.4%
C 8673
 
5.1%
r 8390
 
5.0%
s 7268
 
4.3%
l 6872
 
4.1%
i 6318
 
3.7%
n 5697
 
3.4%
Other values (30) 68368
40.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 169478
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 24263
 
14.3%
a 13063
 
7.7%
t 11481
 
6.8%
o 9085
 
5.4%
C 8673
 
5.1%
r 8390
 
5.0%
s 7268
 
4.3%
l 6872
 
4.1%
i 6318
 
3.7%
n 5697
 
3.4%
Other values (30) 68368
40.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 169478
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 24263
 
14.3%
a 13063
 
7.7%
t 11481
 
6.8%
o 9085
 
5.4%
C 8673
 
5.1%
r 8390
 
5.0%
s 7268
 
4.3%
l 6872
 
4.1%
i 6318
 
3.7%
n 5697
 
3.4%
Other values (30) 68368
40.3%
Distinct128
Distinct (%)0.7%
Missing68
Missing (%)0.4%
Memory size135.5 KiB
2024-04-15T22:18:23.551851image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length100
Median length88
Mean length75.521228
Min length54

Characters and Unicode

Total characters1303874
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBuilding a collaborative project management platform for teams
2nd rowBuilding a recommendation engine based on user behavior analysis
3rd rowDesigning a decentralized social media platform for censorship-resistant communication
4th rowDesigning a decentralized content delivery network (CDN) for distributed content distribution
5th rowDesigning a decentralized autonomous organization (DAO) for decentralized governance
ValueCountFrequency (%)
for 14962
 
9.6%
a 14856
 
9.6%
decentralized 8777
 
5.7%
building 4357
 
2.8%
designing 4292
 
2.8%
implementing 4069
 
2.6%
developing 3686
 
2.4%
platform 3142
 
2.0%
system 2778
 
1.8%
defi 2697
 
1.7%
Other values (298) 91439
59.0%
2024-04-15T22:18:23.956179image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
137790
 
10.6%
e 131521
 
10.1%
i 106301
 
8.2%
n 105848
 
8.1%
a 97973
 
7.5%
t 80735
 
6.2%
o 77547
 
5.9%
r 70281
 
5.4%
l 53275
 
4.1%
s 52312
 
4.0%
Other values (41) 390291
29.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1303874
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
137790
 
10.6%
e 131521
 
10.1%
i 106301
 
8.2%
n 105848
 
8.1%
a 97973
 
7.5%
t 80735
 
6.2%
o 77547
 
5.9%
r 70281
 
5.4%
l 53275
 
4.1%
s 52312
 
4.0%
Other values (41) 390291
29.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1303874
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
137790
 
10.6%
e 131521
 
10.1%
i 106301
 
8.2%
n 105848
 
8.1%
a 97973
 
7.5%
t 80735
 
6.2%
o 77547
 
5.9%
r 70281
 
5.4%
l 53275
 
4.1%
s 52312
 
4.0%
Other values (41) 390291
29.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1303874
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
137790
 
10.6%
e 131521
 
10.1%
i 106301
 
8.2%
n 105848
 
8.1%
a 97973
 
7.5%
t 80735
 
6.2%
o 77547
 
5.9%
r 70281
 
5.4%
l 53275
 
4.1%
s 52312
 
4.0%
Other values (41) 390291
29.9%

STATUS_y
Categorical

Distinct3
Distinct (%)< 0.1%
Missing68
Missing (%)0.4%
Memory size135.5 KiB
Pending
6735 
Rejected
5329 
Approved
5201 

Length

Max length8
Median length8
Mean length7.6099044
Min length7

Characters and Unicode

Total characters131385
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPending
2nd rowApproved
3rd rowApproved
4th rowApproved
5th rowPending

Common Values

ValueCountFrequency (%)
Pending 6735
38.9%
Rejected 5329
30.7%
Approved 5201
30.0%
(Missing) 68
 
0.4%

Length

2024-04-15T22:18:24.106502image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-15T22:18:24.347963image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
pending 6735
39.0%
rejected 5329
30.9%
approved 5201
30.1%

Most occurring characters

ValueCountFrequency (%)
e 27923
21.3%
d 17265
13.1%
n 13470
10.3%
p 10402
 
7.9%
P 6735
 
5.1%
i 6735
 
5.1%
g 6735
 
5.1%
R 5329
 
4.1%
j 5329
 
4.1%
c 5329
 
4.1%
Other values (5) 26133
19.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 131385
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 27923
21.3%
d 17265
13.1%
n 13470
10.3%
p 10402
 
7.9%
P 6735
 
5.1%
i 6735
 
5.1%
g 6735
 
5.1%
R 5329
 
4.1%
j 5329
 
4.1%
c 5329
 
4.1%
Other values (5) 26133
19.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 131385
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 27923
21.3%
d 17265
13.1%
n 13470
10.3%
p 10402
 
7.9%
P 6735
 
5.1%
i 6735
 
5.1%
g 6735
 
5.1%
R 5329
 
4.1%
j 5329
 
4.1%
c 5329
 
4.1%
Other values (5) 26133
19.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 131385
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 27923
21.3%
d 17265
13.1%
n 13470
10.3%
p 10402
 
7.9%
P 6735
 
5.1%
i 6735
 
5.1%
g 6735
 
5.1%
R 5329
 
4.1%
j 5329
 
4.1%
c 5329
 
4.1%
Other values (5) 26133
19.9%
Distinct165
Distinct (%)1.0%
Missing68
Missing (%)0.4%
Memory size135.5 KiB
2024-04-15T22:18:24.536267image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length33
Median length32
Mean length32.374515
Min length31

Characters and Unicode

Total characters558946
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhttp://www.example.com/project85
2nd rowhttp://www.example.com/project109
3rd rowhttp://www.example.com/project128
4th rowhttp://www.example.com/project17
5th rowhttp://www.example.com/project82
ValueCountFrequency (%)
http://www.example.com/project123 303
 
1.8%
http://www.example.com/project31 295
 
1.7%
http://www.example.com/project124 278
 
1.6%
http://www.example.com/project59 229
 
1.3%
http://www.example.com/project137 218
 
1.3%
http://www.example.com/project6 208
 
1.2%
http://www.example.com/project86 204
 
1.2%
http://www.example.com/project146 204
 
1.2%
http://www.example.com/project99 201
 
1.2%
http://www.example.com/project76 201
 
1.2%
Other values (155) 14924
86.4%
2024-04-15T22:18:24.905868image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
p 51795
 
9.3%
/ 51795
 
9.3%
w 51795
 
9.3%
e 51795
 
9.3%
t 51795
 
9.3%
o 34530
 
6.2%
c 34530
 
6.2%
. 34530
 
6.2%
m 34530
 
6.2%
j 17265
 
3.1%
Other values (16) 144586
25.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 558946
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p 51795
 
9.3%
/ 51795
 
9.3%
w 51795
 
9.3%
e 51795
 
9.3%
t 51795
 
9.3%
o 34530
 
6.2%
c 34530
 
6.2%
. 34530
 
6.2%
m 34530
 
6.2%
j 17265
 
3.1%
Other values (16) 144586
25.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 558946
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p 51795
 
9.3%
/ 51795
 
9.3%
w 51795
 
9.3%
e 51795
 
9.3%
t 51795
 
9.3%
o 34530
 
6.2%
c 34530
 
6.2%
. 34530
 
6.2%
m 34530
 
6.2%
j 17265
 
3.1%
Other values (16) 144586
25.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 558946
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p 51795
 
9.3%
/ 51795
 
9.3%
w 51795
 
9.3%
e 51795
 
9.3%
t 51795
 
9.3%
o 34530
 
6.2%
c 34530
 
6.2%
. 34530
 
6.2%
m 34530
 
6.2%
j 17265
 
3.1%
Other values (16) 144586
25.9%
Distinct319
Distinct (%)1.8%
Missing68
Missing (%)0.4%
Memory size135.5 KiB
2024-04-15T22:18:25.141897image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length33
Median length30
Mean length22.057515
Min length13

Characters and Unicode

Total characters380823
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique94 ?
Unique (%)0.5%

Sample

1st rowmmccauley4e@nytimes.com
2nd rowmmccauley4e@nytimes.com
3rd rowmmccauley4e@nytimes.com
4th rowmmccauley4e@nytimes.com
5th rowmmccauley4e@nytimes.com
ValueCountFrequency (%)
bbarbera1x@usatoday.com 1404
 
8.1%
lloadar@miibeian.gov.cn 693
 
4.0%
srigden16@indiegogo.com 486
 
2.8%
gwaslinai@tumblr.com 448
 
2.6%
khaggus43@imageshack.us 385
 
2.2%
nmccurlyeo@uol.com.br 378
 
2.2%
cshelton3t@odnoklassniki.ru 360
 
2.1%
vdahlgrenbg@friendfeed.com 352
 
2.0%
syarrington4o@zdnet.com 294
 
1.7%
agerriessenb1@examiner.com 252
 
1.5%
Other values (309) 12213
70.7%
2024-04-15T22:18:25.540258image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 31568
 
8.3%
e 27832
 
7.3%
a 27120
 
7.1%
c 23422
 
6.2%
r 21569
 
5.7%
m 19864
 
5.2%
n 19364
 
5.1%
. 19310
 
5.1%
i 18807
 
4.9%
l 17337
 
4.6%
Other values (29) 154630
40.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 380823
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 31568
 
8.3%
e 27832
 
7.3%
a 27120
 
7.1%
c 23422
 
6.2%
r 21569
 
5.7%
m 19864
 
5.2%
n 19364
 
5.1%
. 19310
 
5.1%
i 18807
 
4.9%
l 17337
 
4.6%
Other values (29) 154630
40.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 380823
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 31568
 
8.3%
e 27832
 
7.3%
a 27120
 
7.1%
c 23422
 
6.2%
r 21569
 
5.7%
m 19864
 
5.2%
n 19364
 
5.1%
. 19310
 
5.1%
i 18807
 
4.9%
l 17337
 
4.6%
Other values (29) 154630
40.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 380823
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 31568
 
8.3%
e 27832
 
7.3%
a 27120
 
7.1%
c 23422
 
6.2%
r 21569
 
5.7%
m 19864
 
5.2%
n 19364
 
5.1%
. 19310
 
5.1%
i 18807
 
4.9%
l 17337
 
4.6%
Other values (29) 154630
40.6%
Distinct313
Distinct (%)1.8%
Missing68
Missing (%)0.4%
Memory size135.5 KiB
2024-04-15T22:18:25.770545image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length13
Median length11
Mean length6.264234
Min length3

Characters and Unicode

Total characters108152
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique92 ?
Unique (%)0.5%

Sample

1st rowMaximo
2nd rowMaximo
3rd rowMaximo
4th rowMaximo
5th rowMaximo
ValueCountFrequency (%)
brantley 1404
 
8.1%
lettie 693
 
4.0%
shoshanna 486
 
2.8%
vin 472
 
2.7%
grantham 448
 
2.6%
kania 385
 
2.2%
neall 378
 
2.2%
crista 360
 
2.1%
swen 294
 
1.7%
angie 252
 
1.5%
Other values (304) 12125
70.1%
2024-04-15T22:18:26.114933image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 13522
12.5%
e 12088
 
11.2%
n 10354
 
9.6%
i 7773
 
7.2%
r 7178
 
6.6%
l 6858
 
6.3%
t 6637
 
6.1%
o 4768
 
4.4%
y 4089
 
3.8%
h 2864
 
2.6%
Other values (42) 32021
29.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 108152
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 13522
12.5%
e 12088
 
11.2%
n 10354
 
9.6%
i 7773
 
7.2%
r 7178
 
6.6%
l 6858
 
6.3%
t 6637
 
6.1%
o 4768
 
4.4%
y 4089
 
3.8%
h 2864
 
2.6%
Other values (42) 32021
29.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 108152
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 13522
12.5%
e 12088
 
11.2%
n 10354
 
9.6%
i 7773
 
7.2%
r 7178
 
6.6%
l 6858
 
6.3%
t 6637
 
6.1%
o 4768
 
4.4%
y 4089
 
3.8%
h 2864
 
2.6%
Other values (42) 32021
29.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 108152
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 13522
12.5%
e 12088
 
11.2%
n 10354
 
9.6%
i 7773
 
7.2%
r 7178
 
6.6%
l 6858
 
6.3%
t 6637
 
6.1%
o 4768
 
4.4%
y 4089
 
3.8%
h 2864
 
2.6%
Other values (42) 32021
29.6%
Distinct318
Distinct (%)1.8%
Missing68
Missing (%)0.4%
Memory size135.5 KiB
2024-04-15T22:18:26.377023image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length14
Median length13
Mean length6.7867941
Min length3

Characters and Unicode

Total characters117174
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique94 ?
Unique (%)0.5%

Sample

1st rowMcCauley
2nd rowMcCauley
3rd rowMcCauley
4th rowMcCauley
5th rowMcCauley
ValueCountFrequency (%)
barbera 1404
 
8.1%
load 693
 
4.0%
rigden 486
 
2.8%
waslin 448
 
2.6%
haggus 385
 
2.2%
mccurlye 378
 
2.2%
shelton 360
 
2.1%
dahlgren 352
 
2.0%
yarrington 294
 
1.7%
gerriessen 252
 
1.5%
Other values (311) 12218
70.7%
2024-04-15T22:18:26.783811image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 12137
 
10.4%
r 11304
 
9.6%
a 11210
 
9.6%
n 7898
 
6.7%
l 7818
 
6.7%
i 7047
 
6.0%
o 6208
 
5.3%
g 4153
 
3.5%
s 4109
 
3.5%
t 4069
 
3.5%
Other values (41) 41221
35.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 117174
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 12137
 
10.4%
r 11304
 
9.6%
a 11210
 
9.6%
n 7898
 
6.7%
l 7818
 
6.7%
i 7047
 
6.0%
o 6208
 
5.3%
g 4153
 
3.5%
s 4109
 
3.5%
t 4069
 
3.5%
Other values (41) 41221
35.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 117174
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 12137
 
10.4%
r 11304
 
9.6%
a 11210
 
9.6%
n 7898
 
6.7%
l 7818
 
6.7%
i 7047
 
6.0%
o 6208
 
5.3%
g 4153
 
3.5%
s 4109
 
3.5%
t 4069
 
3.5%
Other values (41) 41221
35.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 117174
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 12137
 
10.4%
r 11304
 
9.6%
a 11210
 
9.6%
n 7898
 
6.7%
l 7818
 
6.7%
i 7047
 
6.0%
o 6208
 
5.3%
g 4153
 
3.5%
s 4109
 
3.5%
t 4069
 
3.5%
Other values (41) 41221
35.2%

ROLE_y
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing68
Missing (%)0.4%
Memory size135.5 KiB
user
17265 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters69060
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowuser
2nd rowuser
3rd rowuser
4th rowuser
5th rowuser

Common Values

ValueCountFrequency (%)
user 17265
99.6%
(Missing) 68
 
0.4%

Length

2024-04-15T22:18:26.936149image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-15T22:18:27.035939image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
user 17265
100.0%

Most occurring characters

ValueCountFrequency (%)
u 17265
25.0%
s 17265
25.0%
e 17265
25.0%
r 17265
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 69060
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u 17265
25.0%
s 17265
25.0%
e 17265
25.0%
r 17265
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 69060
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u 17265
25.0%
s 17265
25.0%
e 17265
25.0%
r 17265
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 69060
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u 17265
25.0%
s 17265
25.0%
e 17265
25.0%
r 17265
25.0%

PHONE_NUMBER_y
Real number (ℝ)

Distinct319
Distinct (%)1.8%
Missing68
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean6.0498496 × 109
Minimum1.0049387 × 109
Maximum9.9960208 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size135.5 KiB
2024-04-15T22:18:27.153555image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.0049387 × 109
5-th percentile1.3024693 × 109
Q13.6052395 × 109
median6.2831165 × 109
Q38.6441764 × 109
95-th percentile9.52621 × 109
Maximum9.9960208 × 109
Range8.9910821 × 109
Interquartile range (IQR)5.038937 × 109

Descriptive statistics

Standard deviation2.6813207 × 109
Coefficient of variation (CV)0.44320452
Kurtosis-1.119781
Mean6.0498496 × 109
Median Absolute Deviation (MAD)2.3936422 × 109
Skewness-0.29126312
Sum1.0445065 × 1014
Variance7.1894806 × 1018
MonotonicityNot monotonic
2024-04-15T22:18:27.326706image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9224743888 1404
 
8.1%
3605239489 693
 
4.0%
5811418659 486
 
2.8%
5913549527 448
 
2.6%
9526209988 385
 
2.2%
7129119794 378
 
2.2%
4328864394 360
 
2.1%
7743556574 352
 
2.0%
3587136794 294
 
1.7%
6321637239 252
 
1.5%
Other values (309) 12213
70.5%
ValueCountFrequency (%)
1004938722 96
0.6%
1008868059 1
 
< 0.1%
1066117899 189
1.1%
1092149162 1
 
< 0.1%
1133247041 72
 
0.4%
1172341225 1
 
< 0.1%
1178022515 168
1.0%
1203986793 60
 
0.3%
1287136254 70
 
0.4%
1302469256 210
1.2%
ValueCountFrequency (%)
9996020840 1
 
< 0.1%
9966994472 36
 
0.2%
9961356509 48
 
0.3%
9906928741 64
 
0.4%
9901322391 36
 
0.2%
9877161571 36
 
0.2%
9872962156 160
0.9%
9872017012 180
1.0%
9854767904 1
 
< 0.1%
9789890490 24
 
0.1%
Distinct319
Distinct (%)1.8%
Missing68
Missing (%)0.4%
Memory size135.5 KiB
2024-04-15T22:18:27.555947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length16
Median length13
Mean length11.74457
Min length8

Characters and Unicode

Total characters202770
Distinct characters88
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique94 ?
Unique (%)0.5%

Sample

1st rowyJ2%A#X",ky0Cp
2nd rowyJ2%A#X",ky0Cp
3rd rowyJ2%A#X",ky0Cp
4th rowyJ2%A#X",ky0Cp
5th rowyJ2%A#X",ky0Cp
ValueCountFrequency (%)
yj4)wln9l 1404
 
8.1%
qb7!|%wl@\gp 693
 
4.0%
en4<&*wf 486
 
2.8%
qm9|mq|t`y 448
 
2.6%
uo2",%k5r$b@4l 385
 
2.2%
jb9=ssb/_m 378
 
2.2%
ja1*_,ath3 360
 
2.1%
gh5>xf+>vk 352
 
2.0%
vx6>pkus?!'~n7uc 294
 
1.7%
tf0(h1e_|yjdyw 252
 
1.5%
Other values (309) 12213
70.7%
2024-04-15T22:18:27.974452image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
L 6259
 
3.1%
9 6007
 
3.0%
N 4328
 
2.1%
4 4305
 
2.1%
| 4087
 
2.0%
y 3951
 
1.9%
w 3898
 
1.9%
X 3494
 
1.7%
` 3372
 
1.7%
1 3367
 
1.7%
Other values (78) 159702
78.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 202770
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 6259
 
3.1%
9 6007
 
3.0%
N 4328
 
2.1%
4 4305
 
2.1%
| 4087
 
2.0%
y 3951
 
1.9%
w 3898
 
1.9%
X 3494
 
1.7%
` 3372
 
1.7%
1 3367
 
1.7%
Other values (78) 159702
78.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 202770
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 6259
 
3.1%
9 6007
 
3.0%
N 4328
 
2.1%
4 4305
 
2.1%
| 4087
 
2.0%
y 3951
 
1.9%
w 3898
 
1.9%
X 3494
 
1.7%
` 3372
 
1.7%
1 3367
 
1.7%
Other values (78) 159702
78.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 202770
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 6259
 
3.1%
9 6007
 
3.0%
N 4328
 
2.1%
4 4305
 
2.1%
| 4087
 
2.0%
y 3951
 
1.9%
w 3898
 
1.9%
X 3494
 
1.7%
` 3372
 
1.7%
1 3367
 
1.7%
Other values (78) 159702
78.8%

CHANGE_PASSWORD_y
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing68
Missing (%)0.4%
Memory size135.5 KiB
True
17265 
(Missing)
 
68
ValueCountFrequency (%)
True 17265
99.6%
(Missing) 68
 
0.4%
2024-04-15T22:18:28.112605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

USER_CERTIFICATE_ID
Real number (ℝ)

Distinct868
Distinct (%)5.1%
Missing165
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean422.32922
Minimum2
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size135.5 KiB
2024-04-15T22:18:28.230383image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile45
Q1203
median394
Q3609
95-th percentile940
Maximum1000
Range998
Interquartile range (IQR)406

Descriptive statistics

Standard deviation263.63942
Coefficient of variation (CV)0.62425097
Kurtosis-0.75167524
Mean422.32922
Median Absolute Deviation (MAD)202
Skewness0.39472644
Sum7250548
Variance69505.746
MonotonicityNot monotonic
2024-04-15T22:18:28.381654image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
940 108
 
0.6%
296 108
 
0.6%
67 108
 
0.6%
166 108
 
0.6%
214 108
 
0.6%
280 108
 
0.6%
143 108
 
0.6%
397 108
 
0.6%
483 108
 
0.6%
545 108
 
0.6%
Other values (858) 16088
92.8%
(Missing) 165
 
1.0%
ValueCountFrequency (%)
2 12
 
0.1%
3 54
0.3%
4 12
 
0.1%
5 6
 
< 0.1%
6 48
0.3%
7 8
 
< 0.1%
8 16
 
0.1%
9 16
 
0.1%
10 6
 
< 0.1%
11 15
 
0.1%
ValueCountFrequency (%)
1000 16
0.1%
999 9
 
0.1%
997 20
0.1%
994 25
0.1%
992 12
0.1%
991 12
0.1%
990 24
0.1%
987 1
 
< 0.1%
986 20
0.1%
984 1
 
< 0.1%

CERTIFICATE_ID
Real number (ℝ)

Distinct50
Distinct (%)0.3%
Missing165
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean25.146435
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size135.5 KiB
2024-04-15T22:18:28.525790image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q114
median25
Q338
95-th percentile48
Maximum50
Range49
Interquartile range (IQR)24

Descriptive statistics

Standard deviation14.435273
Coefficient of variation (CV)0.57404849
Kurtosis-1.2186027
Mean25.146435
Median Absolute Deviation (MAD)13
Skewness0.0087239229
Sum431714
Variance208.37711
MonotonicityNot monotonic
2024-04-15T22:18:28.695949image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 591
 
3.4%
44 515
 
3.0%
17 513
 
3.0%
40 512
 
3.0%
36 493
 
2.8%
5 487
 
2.8%
39 439
 
2.5%
31 429
 
2.5%
23 424
 
2.4%
9 409
 
2.4%
Other values (40) 12356
71.3%
ValueCountFrequency (%)
1 353
2.0%
2 360
2.1%
3 395
2.3%
4 345
2.0%
5 487
2.8%
6 381
2.2%
7 282
1.6%
8 282
1.6%
9 409
2.4%
10 395
2.3%
ValueCountFrequency (%)
50 269
1.6%
49 388
2.2%
48 278
1.6%
47 223
1.3%
46 261
1.5%
45 339
2.0%
44 515
3.0%
43 363
2.1%
42 315
1.8%
41 258
1.5%
Distinct331
Distinct (%)1.9%
Missing165
Missing (%)1.0%
Memory size135.5 KiB
Minimum2023-04-06 00:00:00
Maximum2024-04-04 00:00:00
2024-04-15T22:18:28.841669image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:28.994568image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct330
Distinct (%)1.9%
Missing165
Missing (%)1.0%
Memory size135.5 KiB
Minimum2025-04-06 00:00:00
Maximum2026-04-04 00:00:00
2024-04-15T22:18:29.136459image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:29.285610image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

STATUS
Categorical

Distinct3
Distinct (%)< 0.1%
Missing165
Missing (%)1.0%
Memory size135.5 KiB
Pending
7274 
Rejected
5614 
Approved
4280 

Length

Max length8
Median length8
Mean length7.5763048
Min length7

Characters and Unicode

Total characters130070
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPending
2nd rowPending
3rd rowPending
4th rowPending
5th rowPending

Common Values

ValueCountFrequency (%)
Pending 7274
42.0%
Rejected 5614
32.4%
Approved 4280
24.7%
(Missing) 165
 
1.0%

Length

2024-04-15T22:18:29.423428image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-15T22:18:29.681075image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
pending 7274
42.4%
rejected 5614
32.7%
approved 4280
24.9%

Most occurring characters

ValueCountFrequency (%)
e 28396
21.8%
d 17168
13.2%
n 14548
11.2%
p 8560
 
6.6%
P 7274
 
5.6%
i 7274
 
5.6%
g 7274
 
5.6%
R 5614
 
4.3%
j 5614
 
4.3%
c 5614
 
4.3%
Other values (5) 22734
17.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 130070
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 28396
21.8%
d 17168
13.2%
n 14548
11.2%
p 8560
 
6.6%
P 7274
 
5.6%
i 7274
 
5.6%
g 7274
 
5.6%
R 5614
 
4.3%
j 5614
 
4.3%
c 5614
 
4.3%
Other values (5) 22734
17.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 130070
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 28396
21.8%
d 17168
13.2%
n 14548
11.2%
p 8560
 
6.6%
P 7274
 
5.6%
i 7274
 
5.6%
g 7274
 
5.6%
R 5614
 
4.3%
j 5614
 
4.3%
c 5614
 
4.3%
Other values (5) 22734
17.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 130070
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 28396
21.8%
d 17168
13.2%
n 14548
11.2%
p 8560
 
6.6%
P 7274
 
5.6%
i 7274
 
5.6%
g 7274
 
5.6%
R 5614
 
4.3%
j 5614
 
4.3%
c 5614
 
4.3%
Other values (5) 22734
17.5%
Distinct868
Distinct (%)5.1%
Missing165
Missing (%)1.0%
Memory size135.5 KiB
2024-04-15T22:18:29.860983image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters618048
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique86 ?
Unique (%)0.5%

Sample

1st row6a4f2026-d87d-4972-9070-521a7ff3e6ca
2nd row6a4f2026-d87d-4972-9070-521a7ff3e6ca
3rd row6a4f2026-d87d-4972-9070-521a7ff3e6ca
4th row6a4f2026-d87d-4972-9070-521a7ff3e6ca
5th row6a4f2026-d87d-4972-9070-521a7ff3e6ca
ValueCountFrequency (%)
e4bbcdda-b437-4d54-a3e2-49fe16b19486 108
 
0.6%
9fdd3351-f534-4c8f-983a-17a21a147fe8 108
 
0.6%
4711b6ef-bc83-45fb-8360-30b824a9a23a 108
 
0.6%
3422a0a0-fd9e-45ce-86e3-2a17754640ba 108
 
0.6%
7ca3907e-d5d0-4544-bbad-6b37c44c3625 108
 
0.6%
982f3200-2732-4757-8673-41a191fcca5e 108
 
0.6%
4c1be6f8-91c1-42b3-afc3-8e1c6a8138ca 108
 
0.6%
17973c5b-ebc5-4e0d-b4fa-afc8ebb71bd6 108
 
0.6%
2fa763ce-9436-4217-bc28-3c37e78a914b 108
 
0.6%
0dfce7a1-4234-4aa9-85d0-8fd195cc80f2 108
 
0.6%
Other values (858) 16088
93.7%
2024-04-15T22:18:30.185352image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 68672
 
11.1%
4 49233
 
8.0%
8 38107
 
6.2%
b 37126
 
6.0%
a 35042
 
5.7%
9 34181
 
5.5%
1 33958
 
5.5%
7 33149
 
5.4%
f 32835
 
5.3%
c 32687
 
5.3%
Other values (7) 223058
36.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 618048
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 68672
 
11.1%
4 49233
 
8.0%
8 38107
 
6.2%
b 37126
 
6.0%
a 35042
 
5.7%
9 34181
 
5.5%
1 33958
 
5.5%
7 33149
 
5.4%
f 32835
 
5.3%
c 32687
 
5.3%
Other values (7) 223058
36.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 618048
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 68672
 
11.1%
4 49233
 
8.0%
8 38107
 
6.2%
b 37126
 
6.0%
a 35042
 
5.7%
9 34181
 
5.5%
1 33958
 
5.5%
7 33149
 
5.4%
f 32835
 
5.3%
c 32687
 
5.3%
Other values (7) 223058
36.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 618048
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 68672
 
11.1%
4 49233
 
8.0%
8 38107
 
6.2%
b 37126
 
6.0%
a 35042
 
5.7%
9 34181
 
5.5%
1 33958
 
5.5%
7 33149
 
5.4%
f 32835
 
5.3%
c 32687
 
5.3%
Other values (7) 223058
36.1%

CERTIFICATE_NAME
Categorical

Distinct50
Distinct (%)0.3%
Missing165
Missing (%)1.0%
Memory size135.5 KiB
Google Professional Machine Learning Engineer
 
591
VMware Certified Professional - Network Virtualization (VCP-NV)
 
515
Microsoft Certified: Azure Administrator Associate
 
513
Certified Cloud Security Professional (CCSP)
 
512
Cisco Certified DevNet Professional
 
493
Other values (45)
14544 

Length

Max length83
Median length51
Mean length43.402202
Min length10

Characters and Unicode

Total characters745129
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAWS Certified Security - Specialty
2nd rowAWS Certified Security - Specialty
3rd rowAWS Certified Security - Specialty
4th rowAWS Certified Security - Specialty
5th rowAWS Certified Security - Specialty

Common Values

ValueCountFrequency (%)
Google Professional Machine Learning Engineer 591
 
3.4%
VMware Certified Professional - Network Virtualization (VCP-NV) 515
 
3.0%
Microsoft Certified: Azure Administrator Associate 513
 
3.0%
Certified Cloud Security Professional (CCSP) 512
 
3.0%
Cisco Certified DevNet Professional 493
 
2.8%
AWS Certified DevOps Engineer - Professional 487
 
2.8%
Certified Information Security Manager (CISM) 439
 
2.5%
Cisco Certified Network Associate (CCNA) 429
 
2.5%
Microsoft Certified: Azure Data Engineer Associate 424
 
2.4%
Google Professional Cloud Architect 409
 
2.4%
Other values (40) 12356
71.3%

Length

2024-04-15T22:18:30.361865image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
certified 12107
 
13.7%
professional 6965
 
7.9%
associate 4625
 
5.2%
4396
 
5.0%
engineer 3854
 
4.4%
microsoft 3038
 
3.4%
azure 3038
 
3.4%
aws 2603
 
3.0%
google 2570
 
2.9%
security 2558
 
2.9%
Other values (70) 42433
48.1%

Most occurring characters

ValueCountFrequency (%)
e 75350
 
10.1%
71019
 
9.5%
i 64247
 
8.6%
o 49322
 
6.6%
r 49160
 
6.6%
t 45223
 
6.1%
s 37656
 
5.1%
n 32298
 
4.3%
a 31306
 
4.2%
C 29440
 
4.0%
Other values (40) 260108
34.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 745129
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 75350
 
10.1%
71019
 
9.5%
i 64247
 
8.6%
o 49322
 
6.6%
r 49160
 
6.6%
t 45223
 
6.1%
s 37656
 
5.1%
n 32298
 
4.3%
a 31306
 
4.2%
C 29440
 
4.0%
Other values (40) 260108
34.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 745129
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 75350
 
10.1%
71019
 
9.5%
i 64247
 
8.6%
o 49322
 
6.6%
r 49160
 
6.6%
t 45223
 
6.1%
s 37656
 
5.1%
n 32298
 
4.3%
a 31306
 
4.2%
C 29440
 
4.0%
Other values (40) 260108
34.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 745129
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 75350
 
10.1%
71019
 
9.5%
i 64247
 
8.6%
o 49322
 
6.6%
r 49160
 
6.6%
t 45223
 
6.1%
s 37656
 
5.1%
n 32298
 
4.3%
a 31306
 
4.2%
C 29440
 
4.0%
Other values (40) 260108
34.9%

EMAIL
Text

Distinct303
Distinct (%)1.8%
Missing165
Missing (%)1.0%
Memory size135.5 KiB
2024-04-15T22:18:30.577771image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length33
Median length30
Mean length22.075373
Min length13

Characters and Unicode

Total characters378990
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique86 ?
Unique (%)0.5%

Sample

1st rowmmccauley4e@nytimes.com
2nd rowmmccauley4e@nytimes.com
3rd rowmmccauley4e@nytimes.com
4th rowmmccauley4e@nytimes.com
5th rowmmccauley4e@nytimes.com
ValueCountFrequency (%)
bbarbera1x@usatoday.com 1404
 
8.2%
lloadar@miibeian.gov.cn 693
 
4.0%
srigden16@indiegogo.com 486
 
2.8%
gwaslinai@tumblr.com 448
 
2.6%
khaggus43@imageshack.us 385
 
2.2%
nmccurlyeo@uol.com.br 378
 
2.2%
cshelton3t@odnoklassniki.ru 360
 
2.1%
vdahlgrenbg@friendfeed.com 352
 
2.1%
syarrington4o@zdnet.com 294
 
1.7%
agerriessenb1@examiner.com 252
 
1.5%
Other values (293) 12116
70.6%
2024-04-15T22:18:30.967838image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 31458
 
8.3%
e 27700
 
7.3%
a 27013
 
7.1%
c 23320
 
6.2%
r 21534
 
5.7%
m 19787
 
5.2%
n 19261
 
5.1%
. 19208
 
5.1%
i 18707
 
4.9%
l 17224
 
4.5%
Other values (29) 153778
40.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 378990
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 31458
 
8.3%
e 27700
 
7.3%
a 27013
 
7.1%
c 23320
 
6.2%
r 21534
 
5.7%
m 19787
 
5.2%
n 19261
 
5.1%
. 19208
 
5.1%
i 18707
 
4.9%
l 17224
 
4.5%
Other values (29) 153778
40.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 378990
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 31458
 
8.3%
e 27700
 
7.3%
a 27013
 
7.1%
c 23320
 
6.2%
r 21534
 
5.7%
m 19787
 
5.2%
n 19261
 
5.1%
. 19208
 
5.1%
i 18707
 
4.9%
l 17224
 
4.5%
Other values (29) 153778
40.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 378990
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 31458
 
8.3%
e 27700
 
7.3%
a 27013
 
7.1%
c 23320
 
6.2%
r 21534
 
5.7%
m 19787
 
5.2%
n 19261
 
5.1%
. 19208
 
5.1%
i 18707
 
4.9%
l 17224
 
4.5%
Other values (29) 153778
40.6%
Distinct297
Distinct (%)1.7%
Missing165
Missing (%)1.0%
Memory size135.5 KiB
2024-04-15T22:18:31.196015image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length13
Median length11
Mean length6.2680568
Min length3

Characters and Unicode

Total characters107610
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83 ?
Unique (%)0.5%

Sample

1st rowMaximo
2nd rowMaximo
3rd rowMaximo
4th rowMaximo
5th rowMaximo
ValueCountFrequency (%)
brantley 1404
 
8.2%
lettie 693
 
4.0%
shoshanna 486
 
2.8%
vin 472
 
2.7%
grantham 448
 
2.6%
kania 385
 
2.2%
neall 378
 
2.2%
crista 360
 
2.1%
swen 294
 
1.7%
angie 252
 
1.5%
Other values (288) 12028
69.9%
2024-04-15T22:18:31.544775image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 13435
12.5%
e 12018
 
11.2%
n 10312
 
9.6%
i 7708
 
7.2%
r 7140
 
6.6%
l 6822
 
6.3%
t 6629
 
6.2%
o 4751
 
4.4%
y 4071
 
3.8%
h 2863
 
2.7%
Other values (41) 31861
29.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 107610
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 13435
12.5%
e 12018
 
11.2%
n 10312
 
9.6%
i 7708
 
7.2%
r 7140
 
6.6%
l 6822
 
6.3%
t 6629
 
6.2%
o 4751
 
4.4%
y 4071
 
3.8%
h 2863
 
2.7%
Other values (41) 31861
29.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 107610
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 13435
12.5%
e 12018
 
11.2%
n 10312
 
9.6%
i 7708
 
7.2%
r 7140
 
6.6%
l 6822
 
6.3%
t 6629
 
6.2%
o 4751
 
4.4%
y 4071
 
3.8%
h 2863
 
2.7%
Other values (41) 31861
29.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 107610
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 13435
12.5%
e 12018
 
11.2%
n 10312
 
9.6%
i 7708
 
7.2%
r 7140
 
6.6%
l 6822
 
6.3%
t 6629
 
6.2%
o 4751
 
4.4%
y 4071
 
3.8%
h 2863
 
2.7%
Other values (41) 31861
29.6%
Distinct302
Distinct (%)1.8%
Missing165
Missing (%)1.0%
Memory size135.5 KiB
2024-04-15T22:18:31.809937image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length14
Median length13
Mean length6.7848322
Min length3

Characters and Unicode

Total characters116482
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique86 ?
Unique (%)0.5%

Sample

1st rowMcCauley
2nd rowMcCauley
3rd rowMcCauley
4th rowMcCauley
5th rowMcCauley
ValueCountFrequency (%)
barbera 1404
 
8.2%
load 693
 
4.0%
rigden 486
 
2.8%
waslin 448
 
2.6%
haggus 385
 
2.2%
mccurlye 378
 
2.2%
shelton 360
 
2.1%
dahlgren 352
 
2.1%
yarrington 294
 
1.7%
gerriessen 252
 
1.5%
Other values (294) 12118
70.6%
2024-04-15T22:18:32.221028image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 12057
 
10.4%
r 11275
 
9.7%
a 11164
 
9.6%
n 7828
 
6.7%
l 7767
 
6.7%
i 6993
 
6.0%
o 6181
 
5.3%
g 4143
 
3.6%
s 4082
 
3.5%
t 4053
 
3.5%
Other values (42) 40939
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 116482
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 12057
 
10.4%
r 11275
 
9.7%
a 11164
 
9.6%
n 7828
 
6.7%
l 7767
 
6.7%
i 6993
 
6.0%
o 6181
 
5.3%
g 4143
 
3.6%
s 4082
 
3.5%
t 4053
 
3.5%
Other values (42) 40939
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 116482
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 12057
 
10.4%
r 11275
 
9.7%
a 11164
 
9.6%
n 7828
 
6.7%
l 7767
 
6.7%
i 6993
 
6.0%
o 6181
 
5.3%
g 4143
 
3.6%
s 4082
 
3.5%
t 4053
 
3.5%
Other values (42) 40939
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 116482
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 12057
 
10.4%
r 11275
 
9.7%
a 11164
 
9.6%
n 7828
 
6.7%
l 7767
 
6.7%
i 6993
 
6.0%
o 6181
 
5.3%
g 4143
 
3.6%
s 4082
 
3.5%
t 4053
 
3.5%
Other values (42) 40939
35.1%

ROLE
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing165
Missing (%)1.0%
Memory size135.5 KiB
user
17167 
admin
 
1

Length

Max length5
Median length4
Mean length4.0000582
Min length4

Characters and Unicode

Total characters68673
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowuser
2nd rowuser
3rd rowuser
4th rowuser
5th rowuser

Common Values

ValueCountFrequency (%)
user 17167
99.0%
admin 1
 
< 0.1%
(Missing) 165
 
1.0%

Length

2024-04-15T22:18:32.377657image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-15T22:18:32.484477image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
user 17167
> 99.9%
admin 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
u 17167
25.0%
s 17167
25.0%
e 17167
25.0%
r 17167
25.0%
a 1
 
< 0.1%
d 1
 
< 0.1%
m 1
 
< 0.1%
i 1
 
< 0.1%
n 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 68673
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u 17167
25.0%
s 17167
25.0%
e 17167
25.0%
r 17167
25.0%
a 1
 
< 0.1%
d 1
 
< 0.1%
m 1
 
< 0.1%
i 1
 
< 0.1%
n 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 68673
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u 17167
25.0%
s 17167
25.0%
e 17167
25.0%
r 17167
25.0%
a 1
 
< 0.1%
d 1
 
< 0.1%
m 1
 
< 0.1%
i 1
 
< 0.1%
n 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 68673
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u 17167
25.0%
s 17167
25.0%
e 17167
25.0%
r 17167
25.0%
a 1
 
< 0.1%
d 1
 
< 0.1%
m 1
 
< 0.1%
i 1
 
< 0.1%
n 1
 
< 0.1%

PHONE_NUMBER
Real number (ℝ)

Distinct303
Distinct (%)1.8%
Missing165
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean6.0568039 × 109
Minimum1.0049387 × 109
Maximum9.9960208 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size135.5 KiB
2024-04-15T22:18:32.608871image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.0049387 × 109
5-th percentile1.3024693 × 109
Q13.6052395 × 109
median6.2831165 × 109
Q38.6441764 × 109
95-th percentile9.52621 × 109
Maximum9.9960208 × 109
Range8.9910821 × 109
Interquartile range (IQR)5.038937 × 109

Descriptive statistics

Standard deviation2.6816434 × 109
Coefficient of variation (CV)0.44274892
Kurtosis-1.1202888
Mean6.0568039 × 109
Median Absolute Deviation (MAD)2.3936422 × 109
Skewness-0.29269752
Sum1.0398321 × 1014
Variance7.1912112 × 1018
MonotonicityNot monotonic
2024-04-15T22:18:32.756069image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9224743888 1404
 
8.1%
3605239489 693
 
4.0%
5811418659 486
 
2.8%
5913549527 448
 
2.6%
9526209988 385
 
2.2%
7129119794 378
 
2.2%
4328864394 360
 
2.1%
7743556574 352
 
2.0%
3587136794 294
 
1.7%
6321637239 252
 
1.5%
Other values (293) 12116
69.9%
ValueCountFrequency (%)
1004938722 96
 
0.6%
1066117899 189
1.1%
1133247041 72
 
0.4%
1172341225 1
 
< 0.1%
1178022515 168
1.0%
1203986793 60
 
0.3%
1287136254 70
 
0.4%
1302469256 210
1.2%
1308058548 240
1.4%
1315841453 196
1.1%
ValueCountFrequency (%)
9996020840 1
 
< 0.1%
9966994472 36
 
0.2%
9961356509 48
 
0.3%
9906928741 64
 
0.4%
9901322391 36
 
0.2%
9877161571 36
 
0.2%
9872962156 160
0.9%
9872017012 180
1.0%
9789890490 24
 
0.1%
9777598477 45
 
0.3%
Distinct303
Distinct (%)1.8%
Missing165
Missing (%)1.0%
Memory size135.5 KiB
2024-04-15T22:18:32.986861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length16
Median length13
Mean length11.742078
Min length7

Characters and Unicode

Total characters201588
Distinct characters88
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique86 ?
Unique (%)0.5%

Sample

1st rowyJ2%A#X",ky0Cp
2nd rowyJ2%A#X",ky0Cp
3rd rowyJ2%A#X",ky0Cp
4th rowyJ2%A#X",ky0Cp
5th rowyJ2%A#X",ky0Cp
ValueCountFrequency (%)
yj4)wln9l 1404
 
8.2%
qb7!|%wl@\gp 693
 
4.0%
en4<&*wf 486
 
2.8%
qm9|mq|t`y 448
 
2.6%
uo2",%k5r$b@4l 385
 
2.2%
jb9=ssb/_m 378
 
2.2%
ja1*_,ath3 360
 
2.1%
gh5>xf+>vk 352
 
2.1%
vx6>pkus?!'~n7uc 294
 
1.7%
tf0(h1e_|yjdyw 252
 
1.5%
Other values (293) 12116
70.6%
2024-04-15T22:18:33.354487image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
L 6249
 
3.1%
9 5982
 
3.0%
N 4318
 
2.1%
4 4286
 
2.1%
| 4076
 
2.0%
y 3943
 
2.0%
w 3896
 
1.9%
X 3473
 
1.7%
` 3370
 
1.7%
1 3337
 
1.7%
Other values (78) 158658
78.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 201588
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 6249
 
3.1%
9 5982
 
3.0%
N 4318
 
2.1%
4 4286
 
2.1%
| 4076
 
2.0%
y 3943
 
2.0%
w 3896
 
1.9%
X 3473
 
1.7%
` 3370
 
1.7%
1 3337
 
1.7%
Other values (78) 158658
78.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 201588
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 6249
 
3.1%
9 5982
 
3.0%
N 4318
 
2.1%
4 4286
 
2.1%
| 4076
 
2.0%
y 3943
 
2.0%
w 3896
 
1.9%
X 3473
 
1.7%
` 3370
 
1.7%
1 3337
 
1.7%
Other values (78) 158658
78.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 201588
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 6249
 
3.1%
9 5982
 
3.0%
N 4318
 
2.1%
4 4286
 
2.1%
| 4076
 
2.0%
y 3943
 
2.0%
w 3896
 
1.9%
X 3473
 
1.7%
` 3370
 
1.7%
1 3337
 
1.7%
Other values (78) 158658
78.7%

CHANGE_PASSWORD
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing165
Missing (%)1.0%
Memory size135.5 KiB
True
17168 
(Missing)
 
165
ValueCountFrequency (%)
True 17168
99.0%
(Missing) 165
 
1.0%
2024-04-15T22:18:33.487112image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Interactions

2024-04-15T22:18:15.844257image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:06.526805image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:07.932584image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:09.307029image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:10.696196image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:12.169239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:13.161559image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:14.053635image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:14.953815image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:15.942779image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:06.708354image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:08.084346image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:09.463280image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:10.850848image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:12.294014image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:13.261637image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:14.154958image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:15.054400image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:16.043307image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:06.860540image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:08.233989image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:09.614877image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:11.005338image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:12.414592image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:13.360200image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:14.252487image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:15.150342image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:16.140706image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:07.013150image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:08.384462image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:09.770849image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:11.158096image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:12.530413image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:13.461806image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:14.355995image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:15.246944image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:16.367233image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:07.163340image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:08.535484image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:09.922526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:11.307297image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:12.641314image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:13.557398image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:14.454813image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:15.344546image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:16.472177image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:07.318971image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:08.696401image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:10.085753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:11.469533image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:12.754820image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:13.660089image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:14.557377image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:15.446388image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:16.584290image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:07.471143image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:08.851095image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:10.236378image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:11.617657image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:12.855991image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:13.757447image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:14.656768image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:15.544547image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:16.687860image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:07.626778image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:09.003290image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:10.390572image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:11.770038image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:12.961573image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:13.855805image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:14.753629image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:15.644070image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:16.784101image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:07.779969image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:09.154963image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:10.542170image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:11.909432image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:13.062600image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:13.956958image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:14.855696image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-15T22:18:15.742722image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-04-15T22:18:16.988624image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-15T22:18:17.511468image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

USER_SKILL_IDSKILL_IDUSER_IDSKILL_LEVELSTATUS_xSKILL_NAMEEMAIL_xROLE_xPHONE_NUMBER_xPASSWORD_xCHANGE_PASSWORD_xFULL_NAMEUSER_PROJECT_IDPROJECT_NAMEPROJECT_DESCRIPTIONSTATUS_yPROJECT_LINKEMAIL_yFIRST_NAME_xLAST_NAME_xROLE_yPHONE_NUMBER_yPASSWORD_yCHANGE_PASSWORD_yUSER_CERTIFICATE_IDCERTIFICATE_IDVALID_FROMVALID_TILLSTATUSCREDENTIAL_IDCERTIFICATE_NAMEEMAILFIRST_NAME_yLAST_NAME_yROLEPHONE_NUMBERPASSWORDCHANGE_PASSWORD
0133169AdvanceApprovedKanbanmmccauley4e@nytimes.comuser9281548370yJ2%A#X",ky0CpTrueMaximo McCauley93.0ByteHubBuilding a collaborative project management platform for teamsPendinghttp://www.example.com/project85mmccauley4e@nytimes.comMaximoMcCauleyuser9.281548e+09yJ2%A#X",ky0CpTrue321.06.02023-07-212025-07-21Pending6a4f2026-d87d-4972-9070-521a7ff3e6caAWS Certified Security - Specialtymmccauley4e@nytimes.comMaximoMcCauleyuser9.281548e+09yJ2%A#X",ky0CpTrue
1133169AdvanceApprovedKanbanmmccauley4e@nytimes.comuser9281548370yJ2%A#X",ky0CpTrueMaximo McCauley286.0CyberSphereBuilding a recommendation engine based on user behavior analysisApprovedhttp://www.example.com/project109mmccauley4e@nytimes.comMaximoMcCauleyuser9.281548e+09yJ2%A#X",ky0CpTrue321.06.02023-07-212025-07-21Pending6a4f2026-d87d-4972-9070-521a7ff3e6caAWS Certified Security - Specialtymmccauley4e@nytimes.comMaximoMcCauleyuser9.281548e+09yJ2%A#X",ky0CpTrue
2133169AdvanceApprovedKanbanmmccauley4e@nytimes.comuser9281548370yJ2%A#X",ky0CpTrueMaximo McCauley586.0DataBlastDesigning a decentralized social media platform for censorship-resistant communicationApprovedhttp://www.example.com/project128mmccauley4e@nytimes.comMaximoMcCauleyuser9.281548e+09yJ2%A#X",ky0CpTrue321.06.02023-07-212025-07-21Pending6a4f2026-d87d-4972-9070-521a7ff3e6caAWS Certified Security - Specialtymmccauley4e@nytimes.comMaximoMcCauleyuser9.281548e+09yJ2%A#X",ky0CpTrue
3133169AdvanceApprovedKanbanmmccauley4e@nytimes.comuser9281548370yJ2%A#X",ky0CpTrueMaximo McCauley891.0ByteGeniusDesigning a decentralized content delivery network (CDN) for distributed content distributionApprovedhttp://www.example.com/project17mmccauley4e@nytimes.comMaximoMcCauleyuser9.281548e+09yJ2%A#X",ky0CpTrue321.06.02023-07-212025-07-21Pending6a4f2026-d87d-4972-9070-521a7ff3e6caAWS Certified Security - Specialtymmccauley4e@nytimes.comMaximoMcCauleyuser9.281548e+09yJ2%A#X",ky0CpTrue
4133169AdvanceApprovedKanbanmmccauley4e@nytimes.comuser9281548370yJ2%A#X",ky0CpTrueMaximo McCauley1080.0CodeBlazeDesigning a decentralized autonomous organization (DAO) for decentralized governancePendinghttp://www.example.com/project82mmccauley4e@nytimes.comMaximoMcCauleyuser9.281548e+09yJ2%A#X",ky0CpTrue321.06.02023-07-212025-07-21Pending6a4f2026-d87d-4972-9070-521a7ff3e6caAWS Certified Security - Specialtymmccauley4e@nytimes.comMaximoMcCauleyuser9.281548e+09yJ2%A#X",ky0CpTrue
5243158BeginnerApprovedArtificial Intelligencekhaggus43@imageshack.ususer9526209988uO2",%k5r$b@4LTrueKania Haggus102.0TechNestDeveloping a blockchain-based decentralized finance (DeFi) lending platform for peer-to-peer lendingPendinghttp://www.example.com/project108khaggus43@imageshack.usKaniaHaggususer9.526210e+09uO2",%k5r$b@4LTrue19.028.02024-03-182026-03-18Rejectedeb56edba-5a32-45fd-b0a3-24179c78ef44CompTIA Cybersecurity Analyst (CySA+)khaggus43@imageshack.usKaniaHaggususer9.526210e+09uO2",%k5r$b@4LTrue
6243158BeginnerApprovedArtificial Intelligencekhaggus43@imageshack.ususer9526209988uO2",%k5r$b@4LTrueKania Haggus102.0TechNestDeveloping a blockchain-based decentralized finance (DeFi) lending platform for peer-to-peer lendingPendinghttp://www.example.com/project108khaggus43@imageshack.usKaniaHaggususer9.526210e+09uO2",%k5r$b@4LTrue115.016.02024-02-202026-02-20Approved8bed1dff-5678-4325-9ea8-8062da9528aeMicrosoft Certified: Azure Fundamentalskhaggus43@imageshack.usKaniaHaggususer9.526210e+09uO2",%k5r$b@4LTrue
7243158BeginnerApprovedArtificial Intelligencekhaggus43@imageshack.ususer9526209988uO2",%k5r$b@4LTrueKania Haggus102.0TechNestDeveloping a blockchain-based decentralized finance (DeFi) lending platform for peer-to-peer lendingPendinghttp://www.example.com/project108khaggus43@imageshack.usKaniaHaggususer9.526210e+09uO2",%k5r$b@4LTrue162.012.02023-11-152025-11-15Approvedcd766306-045c-45c2-a691-fc177826de52Google Professional Cloud Network Engineerkhaggus43@imageshack.usKaniaHaggususer9.526210e+09uO2",%k5r$b@4LTrue
8243158BeginnerApprovedArtificial Intelligencekhaggus43@imageshack.ususer9526209988uO2",%k5r$b@4LTrueKania Haggus102.0TechNestDeveloping a blockchain-based decentralized finance (DeFi) lending platform for peer-to-peer lendingPendinghttp://www.example.com/project108khaggus43@imageshack.usKaniaHaggususer9.526210e+09uO2",%k5r$b@4LTrue244.048.02023-04-142025-04-14Rejectede8f78122-ec82-4bf5-908b-46fb7f71bdf9Certified ScrumMaster (CSM)khaggus43@imageshack.usKaniaHaggususer9.526210e+09uO2",%k5r$b@4LTrue
9243158BeginnerApprovedArtificial Intelligencekhaggus43@imageshack.ususer9526209988uO2",%k5r$b@4LTrueKania Haggus102.0TechNestDeveloping a blockchain-based decentralized finance (DeFi) lending platform for peer-to-peer lendingPendinghttp://www.example.com/project108khaggus43@imageshack.usKaniaHaggususer9.526210e+09uO2",%k5r$b@4LTrue256.031.02024-02-072026-02-07Rejected5ca8a625-79f9-480f-8770-32471203ede7Cisco Certified Network Associate (CCNA)khaggus43@imageshack.usKaniaHaggususer9.526210e+09uO2",%k5r$b@4LTrue
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17324109748239AdvancePendingRoboticsfblondel6c@cam.ac.ukuser3794940074xL6@..H#u@&Uhv3TrueFredrick Blondel983.0TechTonicDeveloping a blockchain-based decentralized finance (DeFi) lending platform for peer-to-peer lendingPendinghttp://www.example.com/project106fblondel6c@cam.ac.ukFredrickBlondeluser3.794940e+09xL6@..H#u@&Uhv3True399.011.02023-10-172025-10-17Rejecteda42ad6c5-06d2-4abc-9efe-27b26a4b7206Google Professional Cloud Developerfblondel6c@cam.ac.ukFredrickBlondeluser3.794940e+09xL6@..H#u@&Uhv3True
17325109748239AdvancePendingRoboticsfblondel6c@cam.ac.ukuser3794940074xL6@..H#u@&Uhv3TrueFredrick Blondel983.0TechTonicDeveloping a blockchain-based decentralized finance (DeFi) lending platform for peer-to-peer lendingPendinghttp://www.example.com/project106fblondel6c@cam.ac.ukFredrickBlondeluser3.794940e+09xL6@..H#u@&Uhv3True406.07.02023-08-212025-08-21Pendinga429f5e9-ecb1-4dae-904a-f38ecfff0f1eAWS Certified Advanced Networking - Specialtyfblondel6c@cam.ac.ukFredrickBlondeluser3.794940e+09xL6@..H#u@&Uhv3True
17326109748239AdvancePendingRoboticsfblondel6c@cam.ac.ukuser3794940074xL6@..H#u@&Uhv3TrueFredrick Blondel983.0TechTonicDeveloping a blockchain-based decentralized finance (DeFi) lending platform for peer-to-peer lendingPendinghttp://www.example.com/project106fblondel6c@cam.ac.ukFredrickBlondeluser3.794940e+09xL6@..H#u@&Uhv3True446.047.02023-06-082025-06-08Pending1133add4-97fd-472c-a85c-84a6c77a046aVMware Certified Design Expert (VCDX)fblondel6c@cam.ac.ukFredrickBlondeluser3.794940e+09xL6@..H#u@&Uhv3True
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1732911008223BeginnerPendingNode.jsgskirving5w@about.comuser1668639496qM5+\G`XXTrueGabrielle Skirving357.0CodeCraftersDesigning a decentralized autonomous organization (DAO) for decentralized governanceRejectedhttp://www.example.com/project161gskirving5w@about.comGabrielleSkirvinguser1.668639e+09qM5+\G`XXTrue48.011.02023-05-182025-05-18Pending38b4146f-e079-437d-a00a-15a665beb720Google Professional Cloud Developergskirving5w@about.comGabrielleSkirvinguser1.668639e+09qM5+\G`XXTrue
1733011008223BeginnerPendingNode.jsgskirving5w@about.comuser1668639496qM5+\G`XXTrueGabrielle Skirving357.0CodeCraftersDesigning a decentralized autonomous organization (DAO) for decentralized governanceRejectedhttp://www.example.com/project161gskirving5w@about.comGabrielleSkirvinguser1.668639e+09qM5+\G`XXTrue318.031.02023-10-222025-10-22Rejected24524ff9-399d-454e-89d3-23935d251bb9Cisco Certified Network Associate (CCNA)gskirving5w@about.comGabrielleSkirvinguser1.668639e+09qM5+\G`XXTrue
1733111008223BeginnerPendingNode.jsgskirving5w@about.comuser1668639496qM5+\G`XXTrueGabrielle Skirving357.0CodeCraftersDesigning a decentralized autonomous organization (DAO) for decentralized governanceRejectedhttp://www.example.com/project161gskirving5w@about.comGabrielleSkirvinguser1.668639e+09qM5+\G`XXTrue543.049.02023-08-132025-08-13Approved38868738-c71d-401a-8a4e-8d8f853742c1Project Management Professional (PMP)gskirving5w@about.comGabrielleSkirvinguser1.668639e+09qM5+\G`XXTrue
1733211008223BeginnerPendingNode.jsgskirving5w@about.comuser1668639496qM5+\G`XXTrueGabrielle Skirving357.0CodeCraftersDesigning a decentralized autonomous organization (DAO) for decentralized governanceRejectedhttp://www.example.com/project161gskirving5w@about.comGabrielleSkirvinguser1.668639e+09qM5+\G`XXTrue909.08.02023-12-172025-12-17Pending53e23f16-c5a2-4a66-b475-9ec0f67acd6eGoogle Associate Cloud Engineergskirving5w@about.comGabrielleSkirvinguser1.668639e+09qM5+\G`XXTrue